Exercise Solutions Introduction

Exercise

1. Combine all elements of df_list to one data frame. The result should be only a single line of code.

• do.call(rbind.data.frame, df_list)
  • do.call pass elements of df_list as arguments to rbind. It’s equivalent of rbind(df_list[[1]], df_list[[2]], df_list[[3]], ....., df_list[[length of df_list]]).

2. Fix each of the following common data frame subsetting errors:

• mtcars[mtcars$cyl == 4, ]
• mtcars[-c(1:4), ]
• mtcars[mtcars$cyl <= 5, ]
• mtcars[mtcars$cyl == 4 | mtcars$cyl == 6, ]

3. What does df[is.na(df)] <- 0 do

• replaces all NAs with 0 but keeps the dimensions.

4. How would you randomly permute the columns of a data frame?

• df[sample(1:nrow(df)), ]

5. Write a while and a repeat loop doing the same as

for(i in LETTERS[1:10]){
  print(i)
}

## [1] "A"
## [1] "B"
## [1] "C"
## [1] "D"
## [1] "E"
## [1] "F"
## [1] "G"
## [1] "H"
## [1] "I"
## [1] "J"

# while 
i <- 0
while (i < 10) {
  i = i + 1
  print(LETTERS[i])
  
}

## [1] "A"
## [1] "B"
## [1] "C"
## [1] "D"
## [1] "E"
## [1] "F"
## [1] "G"
## [1] "H"
## [1] "I"
## [1] "J"

# alternative 
i <- 1
while (i < 11) {
  print(LETTERS[i])
  i = i + 1 
}

## [1] "A"
## [1] "B"
## [1] "C"
## [1] "D"
## [1] "E"
## [1] "F"
## [1] "G"
## [1] "H"
## [1] "I"
## [1] "J"

# repeat
i <- 0
repeat{
  i = i + 1
  print(LETTERS[i])
  if (i > 9) {
    break
  }
  
}

## [1] "A"
## [1] "B"
## [1] "C"
## [1] "D"
## [1] "E"
## [1] "F"
## [1] "G"
## [1] "H"
## [1] "I"
## [1] "J"

# alternative
i <- 1
repeat{
  print(LETTERS[i])
  i = i + 1 
  if (i > 10) {
    break
  }
}

## [1] "A"
## [1] "B"
## [1] "C"
## [1] "D"
## [1] "E"
## [1] "F"
## [1] "G"
## [1] "H"
## [1] "I"
## [1] "J"

6. Write a for loop doing the same as

count <- 0
repeat{
  x     <- sample(1:6, 1)
  count <- count + 1 
  if(x == 6) break
}
print(count)

## [1] 21

for (i in 1:100) {
  x     <- sample(1:6, 1)
  count <- i
  if(x == 6) break
}
print(count)

## [1] 6

• note that \(n\) has to be high enough

7. What is the problem here?

• Curly braces are open and closed before the statement is passed to the function

8. What is the problem here? Can you debug it?

• df will not be passed to the final call and fast_lm is the wrong call

df <- mtcars

lin_mod <- function(df){
lm(y ~ . ,data = df)
}

simple_lin_mod <- function(data, y, x){
df <- data[ ,c(y, x)]
names(df) <- c("y", "x")
lin_mod(df)
}

simple_lin_mod(df, "mpg", "cyl")

## 
## Call:
## lm(formula = y ~ ., data = df)
## 
## Coefficients:
## (Intercept)            x  
##      37.885       -2.876